4 citations · 17 across the 12 of their papers we have counts for
12 papers
Learning Probabilities of Causation with Mask-Augmented Data
Shuai Wang, Yizhou Sun, Judea Pearl +1
Probabilities of causation play a central role in modern decision making. Tian and Pearl first introduced formal definitions and derived tight bounds for three binary probabilities…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…
Epsilon-Identifiability of Causal Quantities
Ang Li, Scott Mueller, Judea Pearl
Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from th…
Probabilities of Causation: Role of Observational Data
Ang Li, Judea Pearl
Probabilities of causation play a crucial role in modern decision-making. Pearl defined three binary probabilities of causation, the probability of necessity and sufficiency (PNS),…
Unit Selection: Learning Benefit Function from Finite Population Data
Ang Li, Song Jiang, Yizhou Sun +1
The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one…
Unit Selection: Case Study and Comparison with A/B Test Heuristic
Ang Li, Judea Pearl
The unit selection problem defined by Li and Pearl identifies individuals who have desired counterfactual behavior patterns, for example, individuals who would respond positively i…
Probabilities of Causation: Adequate Size of Experimental and Observational Samples
Ang Li, Ruirui Mao, Judea Pearl
The probabilities of causation are commonly used to solve decision-making problems. Tian and Pearl derived sharp bounds for the probability of necessity and sufficiency (PNS), the…
Unit Selection with Nonbinary Treatment and Effect
Ang Li, Judea Pearl
The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one…
Probabilities of Causation with Nonbinary Treatment and Effect
Ang Li, Judea Pearl
This paper deals with the problem of estimating the probabilities of causation when treatment and effect are not binary. Tian and Pearl derived sharp bounds for the probability of…
PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning
Junfeng Guo, Ang Li, Cong Liu
While real-world applications of reinforcement learning are becoming popular, the security and robustness of RL systems are worthy of more attention and exploration. In particular,…
Bounds on Causal Effects and Application to High Dimensional Data
Ang Li, Judea Pearl
This paper addresses the problem of estimating causal effects when adjustment variables in the back-door or front-door criterion are partially observed. For such scenarios, we deri…
Causes of Effects: Learning individual responses from population data
Scott Mueller, Ang Li, Judea Pearl
The problem of individualization is recognized as crucial in almost every field. Identifying causes of effects in specific events is likewise essential for accurate decision making…